Discovering Temporal Patterns of Differential Gene Expression in Microarray 
Time Series

Stegle, O; Denby, KJ; Wild, DL; McHattie, S; Mead, A; Ghahramani, Z; Borgwardt, KM

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Discovering Temporal Patterns of Differential Gene Expression in Microarray Time Series

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Stegle, O
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Former Research Group Machine Learning and Computational Biology, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Molecular Biology, Max Planck Institute for Developmental Biology, Max Planck Society;

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Borgwardt, KM
Max Planck Institute for Biological Cybernetics, Max Planck Society;
Former Research Group Machine Learning and Computational Biology, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Department Molecular Biology, Max Planck Institute for Developmental Biology, Max Planck Society;

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引用

Stegle, O., Denby, K., Wild, D., McHattie, S., Mead, A., Ghahramani, Z., & Borgwardt, K. (2009). Discovering Temporal Patterns of Differential Gene Expression in Microarray Time Series. In I., Grosse, S., Neumann, S., Posch, F., Schreiber, & P., Stadler (Eds.), German Conference on Bioinformatics 2009 (GCB 2009) (pp. 133-142). Bonn, Germany: Gesellschaft für Informatik.

引用: https://hdl.handle.net/21.11116/0000-000A-5809-A

要旨

A wealth of time series of microarray measurements have become available over recent years. Several two-sample tests for detecting differential gene expression
in these time series have been defined, but they can only answer the question whether a gene is differentially expressed across the whole time series, not in which intervals it is differentially expressed. In this article, we propose a Gaussian process based approach for studying these dynamics of differential gene expression. In experiments on Arabidopsis thaliana gene expression levels, our novel technique helps us to uncover that the family of WRKY transcription factors appears to be involved in the early response to infection by a fungal pathogen.